@article {68938,
	title = {Learning produces an orthogonalized state machine in the hippocampus.},
	journal = {Nature},
	volume = {640},
	year = {2025},
	month = {2025 February 12},
	abstract = {<p>Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.</p>
},
	doi = {10.1038/s41586-024-08548-w},
	url = {https://doi.org/10.1038/s41586-024-08548-w},
	author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson}
}
@article {47551,
	title = {A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging.},
	journal = {eLife},
	volume = {5},
	year = {2016},
	month = {2016 Jun 14},
	pages = {e14472},
	abstract = {<p>Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (\&lt;1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (∅ 5 mm x 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast three-dimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.</p>
},
	issn = {2050-084X},
	doi = {10.7554/eLife.14472},
	author = {Sofroniew, Nicholas James and Flickinger, Daniel and King, Jonathon and Svoboda, Karel}
}
